Systematic review of clinical prediction models for survival after surgery for resectable pancreatic cancer. BJS 2019; 106: 342-354.
Published: 13th February 2019
Authors: M. Strijker, J. W. Chen, T. H. Mungroop, N. B. Jamieson, C. H. van Eijck, E. W. Steyerberg et al.
As more therapeutic options for pancreatic cancer are becoming available, there is a need to improve outcome prediction to support shared decision‐making. A systematic evaluation of prediction models in resectable pancreatic cancer is lacking.
This systematic review followed the CHARMS and PRISMA guidelines. PubMed, Embase and Cochrane Library databases were searched up to 11 October 2017. Studies reporting development or validation of models predicting survival in resectable pancreatic cancer were included. Models without performance measures, reviews, abstracts or more than 10 per cent of patients not undergoing resection in postoperative models were excluded. Studies were appraised critically.
After screening 4403 studies, 22 (44 319 patients) were included. There were 19 model development/update studies and three validation studies, altogether concerning 21 individual models. Two studies were deemed at low risk of bias. Eight models were developed for the preoperative setting and 13 for the postoperative setting. Most frequently included parameters were differentiation grade (11 of 21 models), nodal status (8 of 21) and serum albumin (7 of 21). Treatment‐related variables were included in three models. The C‐statistic/area under the curve values ranged from 0·57 to 0·90. Based on study design, validation methods and the availability of web‐based calculators, two models were identified as the most promising.
Although a large number of prediction models for resectable pancreatic cancer have been reported, most are at high risk of bias and have not been validated externally. This overview of prognostic factors provided practical recommendations that could help in designing easily applicable prediction models to support shared decision‐making.Full text
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